Study overview


The Master "Industrial Engineering" (WIN) is an interdisciplinary course of studies consisting of engineering and economic parts.

The challenges for companies lie in the permanent innovation of existing products and services in order to successfully compete in the global market. For this purpose, it is necessary to combine sophisticated technology with economic thinking. The master's program therefore contains both technical modules (e.g. "Smart Machines" and "Smart Material Scienes") and modules with an economic focus.

Digital transformation is currently a key focus in strategic corporate development. The master's program "Industrial Engineering" provides the necessary skills to actively shape the future.

Graduates of the master's program are able to apply the acquired competencies independently and on their own responsibility in their future professional practice. A wide range of career opportunities open up across all sectors in middle and senior management.

Short formWIN
Type of studyfull time / Part time
Standard period of study3 semester
AwardMaster of Engineering (M.Eng.)
Start of studieswinter semester and summer semester
Admission restrictions  specific
Lecture locationAnsbach, Rothenburg, Blended Learning
Language of instructionGerman
Course management Prof. Dr.-Ing. Alexandru Sover
Student advisory service

Prof. Dr.-Ing. Alexandru Sover
Prof. Dr.-Ing. Jürgen Göhringer

Student Servicesstudierendenservice.win(at)hs-ansbach.de

Admission requirements and application to study

The Master's programme in Industrial Engineering and Management (WIN) always starts in the winter and summer semester.

The prerequisite for application is a successfully completed university degree in a relevant degree programme with an overall examination grade of at least 2.2, the scope of which usually comprises 210 ECTS credits. Relevant courses of study are engineering courses such as industrial engineering, electrical engineering, mechanical engineering, plastics technology and (business) informatics.

You can find all information on applying  HERE.

The Bavarian Higher Education Act Bayerische Hochschulgesetz (BayHSchG) applies to applicants at Bavarian universities.

Study structure

The Master's programme "Industrial Engineering and Management" comprises 90 ECTS divided into three semesters. There are modules that have a technical or economic focus. A number of courses link the technical aspects with a business perspective.

Individual profiling is possible through elective modules. The extensive team-oriented project work is excellent preparation for later professional life, as a task is structured and worked on independently in cooperation with fellow students. In particular, the topics relevant to companies such as digitalisation (Industry 4.0), artificial intelligence, smart materials and sustainable business will play an important role in the degree programme. For this purpose, several modern laboratories with machines and software are also available to deepen the theoretical topics in a practical environment. The degree programme is completed with a scientific Master's thesis, which is often written in cooperation with an industrial partner. After successful completion of the programme, the internationally recognised academic degree Master of Engineering (M.Eng.) is awarded.

Perspectives

The Master's programme in Industrial Engineering and Management is highly recognised by companies in all sectors. In particular, through the combination of technology and economic know-how, it opens up excellent opportunities to take on leadership responsibility up to top management in the course of one's professional life. This applies to jobs in small and medium-sized companies as well as in internationally operating corporations. The path to research and public administration is also open. Thus, a wide variety of industries are the future field of activity:

  • Automotive
  • Aircraft industry
  • Mechanical engineering / electrical engineering
  • Plastics industry
  • Chemical, pharmaceutical and food industry
  • Research institutions

Career opportunities lie in various company departments such as development, production, sales or management consultancy. For example, the following positions are suitable:

  • Business unit management
  • (Partial) development management
  • Chief Technology Officer (CTO)
  • Production management
  • Project management
  • Management consultancy

Staff

Prof. Dr.-Ing. Alexandru Sover – Studiengangsleiter Master Wirtschaftsingenieurwesen (WIN)

Prof. Dr.-Ing. Alexandru Sover

Studiengangsleiter Master Wirtschaftsingenieurwesen (WIN)

0981 4877-527 51.1.21 nach Vereinbarung vCard

Prof. Dr.-Ing. Alexandru Sover

Prof. Dr.-Ing. Alexandru Sover – Studiengangsleiter Master Wirtschaftsingenieurwesen (WIN)

Studiengangsleiter Master Wirtschaftsingenieurwesen (WIN)

Funktionen:

  • Studiengangsleiter Master Wirtschaftsingenieurwesen (WIN)
  • Vorsitzender Prüfungskommission Master Wirtschaftsingenieurwesen (WIN)
  • Mitglied Fakultätsrat Technik
  • Laserschutzbeauftragter der Hochschule Ansbach
  • Professor für Verbundwerkstoffe

Lehrgebiete:

  • Werkstofftechnik
  • Kunststofftechnik
  • Kunststoffverarbeitung
  • Spezielle Verarbeitungstechnik
  • Prüftechnik
  • Analyseverfahren
  • Verbindungstechnik
  • Prototyping und Design

Forschungsfelder:

  • Kunststoffverarbeitung
  • Kunststoffprüfung, -analyse
  • Laserbearbeitung
  • Entlackung von Kunststoffbauteilen
  • Entschichten von Hybridmaterialien
  • Additive Manufacturing (FDM/FFF; SLS, SLA)
  • Produktentwicklung

Publikationen

siehe Liste

Ralph-Peter Kappestein – Leiter Studierendenservice der School of Business and Technology (SBT)

Ralph-Peter Kappestein

Leiter Studierendenservice der School of Business and Technology (SBT)

0981 4877-143 BHS 3.02 (Brauhausstraße 15, 91522 Ansbach) nach Vereinbarung vCard

Ralph-Peter Kappestein

Ralph-Peter Kappestein – Leiter Studierendenservice der School of Business and Technology (SBT)

Leiter Studierendenservice der School of Business and Technology (SBT)

Funktionen:

  • Leiter Studierendenservice der School of Business and Technology (SBT)
Prof. Dr.-Ing. Jürgen Göhringer – Professor Wirtschaftsingenieurwesen (WIN)

Prof. Dr.-Ing. Jürgen Göhringer

Professor Wirtschaftsingenieurwesen (WIN)

0981 4877-573 51.1.7 nach Vereinbarung vCard

Prof. Dr.-Ing. Jürgen Göhringer

Prof. Dr.-Ing. Jürgen Göhringer – Professor Wirtschaftsingenieurwesen (WIN)

Professor Wirtschaftsingenieurwesen (WIN)

Funktionen:

  • Professor Wirtschaftsingenieurwesen (WIG)
  • Professor Wirtschaftsingenieurwesen (WIN)
  • Wissenschaftlicher Leiter Institut für Mittelstand und UnternehmensEntwicklung Ansbach (IMEA)

Lehrgebiete:

  • Automatisierungstechnik
  • Digitalisierung in der Industrie
  • Manufacturing Execution Systems (MES)
  • Grundlagen der Informationstechnologie
  • Informatik

Forschung und Weiterbildung:

  • Digitalisierung, IoT, Digitale Transformation
  • Manufacturing Execution Systems, Scheduling
  • Strategieentwicklung (Masterplan, KPIs, Scorecard)

Vita:

  • Professor, Hochschule Ansbach
  • Leiter Strategie, Siemens AG, Digtial Factory
  • Leiter Business Development IT,Siemens AG, Digital Factory
  • Projektleiter, Leiter Consulting MES, Siemens AG, Software House
  • Wissenschaftlicher Assistent, Lehrstuhl FAPS, Universität Erlangen
  • Studium Maschinenbau, Universität Erlangen

Publikationen (Auszug):

  • Die Digitalisierung treibt MES, FAPS-IPC Fachtagungsband 2017
  • Ohne Strategie gibt es keine erfolgreiche Digitalisierung, FAPS-IPC Fachtagungsband 2017
  • Systematische Entwicklung und internationaler Roll-out innovativer Dienstleistungen, macrusevans, 2011
  • Produktionsoptimierung senkt Kosten, VDI-Z, 2008
  • Systematische Entwicklung und internationaler Roll-out eines Dienstleistungsportfolios,  Aachener Dienstleistungsforum, RWTH Aachen, 2008
  • Handbook of Industrial Engineering, Section Manufacturing and Production Systems, John Wiley & Sons, Inc.; New York, 3rd Edition, 2001
Prof. Dr.-Ing. Simon Hufnagel – Professor Künstliche Intelligenz und Kognitive Systeme (KIK)

Prof. Dr.-Ing. Simon Hufnagel

Professor Künstliche Intelligenz und Kognitive Systeme (KIK)

0981 4877-411 50.0.3 nach Vereinbarung vCard

Prof. Dr.-Ing. Simon Hufnagel

Prof. Dr.-Ing. Simon Hufnagel – Professor Künstliche Intelligenz und Kognitive Systeme (KIK)

Professor Künstliche Intelligenz und Kognitive Systeme (KIK)

Funktionen:

Professor Künstliche Intelligenz und Kognitive Systeme (KIK)

Prof. Dr.-Ing. Thomas Müller-Lenhardt – Studiengangsleiter Angewandte Kunststofftechnik (AKT)

Prof. Dr.-Ing. Thomas Müller-Lenhardt

Studiengangsleiter Angewandte Kunststofftechnik (AKT)

09141 874669-305 50.0.2 nach Vereinbarung vCard

Prof. Dr.-Ing. Thomas Müller-Lenhardt

Prof. Dr.-Ing. Thomas Müller-Lenhardt – Studiengangsleiter Angewandte Kunststofftechnik (AKT)

Studiengangsleiter Angewandte Kunststofftechnik (AKT)

Funktionen:

  • Professor für Kunststofftechnik
  • Studiengangsleiter Angewandte Kunststofftechnik (AKT)
  • Vorsitzender Prüfungskommission Angewandte Kunststofftechnik
  • Stellvertretender wissenschaftlicher Leiter Studienzentrum „kunststoffcampus bayern“ Weißenburg (WUG)

Lehrgebiete:

  • Analyseverfahren
  • Werkstoffkunde
  • Kunststofftechnik
  • Kunststoffverarbeitung
  • Spezielle Verarbeitungstechnik
  • Prüftechnik
  • Verbindungstechnik
  • Entwicklungsstrategien
  • Faserverbundkunststoffe
Prof. Dr. Torsten Schmidt – Professor Wirtschaftsingenieurwesen (WIN)

Prof. Dr. Torsten Schmidt

Professor Wirtschaftsingenieurwesen (WIN)

0981 4877-262 51.1.5 nach Vereinbarung vCard

Prof. Dr. Torsten Schmidt

Prof. Dr. Torsten Schmidt – Professor Wirtschaftsingenieurwesen (WIN)

Professor Wirtschaftsingenieurwesen (WIN)

Funktionen:

  • Studiengangsleiter Künstliche Intelligenz und Kognitive Systeme (KIK)
  • Professor Wirtschaftsingenieurwesen (WIG)
  • Mitglied Fakultätsrat Technik
  • Beauftragter für Studierende mit Behinderung und chronischen Erkrankungen

Lehrgebiete:

  • Künstliche Intelligenz / Maschinelles Lernen
  • Deep Medicine
  • Automatisierungstechnik
  • Mathematik 1 / 2
  • Physik 1 / 2

Forschungsgebiete:

  • Natürliche Sprachassistenten und deren Anwendungen
  • Effektive Lernalgorithmen für kleine und kleinste Datenmengen
  • KI Greybox-Modelle / Modellprädiktive Algorithmen
  • Algorithmen des Human Arguing und Human Reasoning